Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
68 lines
3.2 KiB
Python
68 lines
3.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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### <summary>
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### This algorithm demonstrates how to submit orders to a Financial Advisor account group, allocation profile or a single managed account.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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### <meta name="tag" content="financial advisor" />
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class FinancialAdvisorDemoAlgorithm(QCAlgorithm):
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def Initialize(self):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
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self.SetStartDate(2013,10,7) #Set Start Date
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self.SetEndDate(2013,10,11) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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self.symbol = self.AddEquity("SPY", Resolution.Second).Symbol
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# The default order properties can be set here to choose the FA settings
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# to be automatically used in any order submission method (such as SetHoldings, Buy, Sell and Order)
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# Use a default FA Account Group with an Allocation Method
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self.DefaultOrderProperties = InteractiveBrokersOrderProperties()
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# account group created manually in IB/TWS
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self.DefaultOrderProperties.FaGroup = "TestGroupEQ"
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# supported allocation methods are: EqualQuantity, NetLiq, AvailableEquity, PctChange
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self.DefaultOrderProperties.FaMethod = "EqualQuantity"
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# set a default FA Allocation Profile
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# DefaultOrderProperties = InteractiveBrokersOrderProperties()
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# allocation profile created manually in IB/TWS
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# self.DefaultOrderProperties.FaProfile = "TestProfileP"
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# send all orders to a single managed account
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# DefaultOrderProperties = InteractiveBrokersOrderProperties()
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# a sub-account linked to the Financial Advisor master account
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# self.DefaultOrderProperties.Account = "DU123456"
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def OnData(self, data):
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# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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if not self.Portfolio.Invested:
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# when logged into IB as a Financial Advisor, this call will use order properties
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# set in the DefaultOrderProperties property of QCAlgorithm
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self.SetHoldings("SPY", 1)
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